Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs

• URL: http://arxiv.org/abs/2511.14343v1
• Date: Tue, 18 Nov 2025 10:50:04 GMT
• Title: Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs
• Authors: Yiyi Miao, Taoyu Wu, Ji Jiang, Tong Chen, Zhe Tang, Zhengyong Jiang, Angelos Stefanidis, Limin Yu, Jionglong Su,
• Abstract summary: We propose DentalSCR, a pose-stable, contour-guided framework for accurate and interpretable silhouette-to-contour registration.<n>We evaluate DentalSCR on 34 expert-annotated clinical cases.
• Score: 10.70146635420186
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reliable 3D-2D alignment between intraoral scan (IOS) models and lateral cephalometric radiographs is critical for orthodontic diagnosis, yet conventional intensity-driven registration methods struggle under real clinical conditions, where cephalograms exhibit projective magnification, geometric distortion, low-contrast dental crowns, and acquisition-dependent variation. These factors hinder the stability of appearance-based similarity metrics and often lead to convergence failures or anatomically implausible alignments. To address these limitations, we propose DentalSCR, a pose-stable, contour-guided framework for accurate and interpretable silhouette-to-contour registration. Our method first constructs a U-Midline Dental Axis (UMDA) to establish a unified cross-arch anatomical coordinate system, thereby stabilizing initialization and standardizing projection geometry across cases. Using this reference frame, we generate radiograph-like projections via a surface-based DRR formulation with coronal-axis perspective and Gaussian splatting, which preserves clinical source-object-detector magnification and emphasizes external silhouettes. Registration is then formulated as a 2D similarity transform optimized with a symmetric bidirectional Chamfer distance under a hierarchical coarse-to-fine schedule, enabling both large capture range and subpixel-level contour agreement. We evaluate DentalSCR on 34 expert-annotated clinical cases. Experimental results demonstrate substantial reductions in landmark error-particularly at posterior teeth-tighter dispersion on the lower jaw, and low Chamfer and controlled Hausdorff distances at the curve level. These findings indicate that DentalSCR robustly handles real-world cephalograms and delivers high-fidelity, clinically inspectable 3D--2D alignment, outperforming conventional baselines.

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